Breno Ortega Fernandez

This study proposes a method to indicate the optimal moment to interrupt grinding wheel dressing using the counts statistics extracted from acoustic emission signals. Experiments were conducted on a surface grinding machine equipped with an aluminum oxide grinding wheel and a single-point diamond dresser. A Multilayer Perceptron neural network, trained via the Scaled Conjugate Gradient algorithm, classified the grinding wheel condition into three states: undressed, intermediate, and dressed. In three external tests not included in the training set, the model achieved an accuracy of 88.13%.

The study proposes a new methodology based on time-frequency analysis for the indirect monitoring of the dressing operation of conventional grinding wheels. Through a low-cost piezoelectric diaphragm (PZT), acoustic signals are captured during the process. The analysis is based on the coefficient of variation in the Short-Time Fourier Transform (STFT). The results indicate that the signal instability is high in the first passes but progressively decreases, reaching stability between passes 10 and 15. This suggests that the surface of the grinding wheel is regularized and ready for grinding. The methodology can serve as an objective indicator to assist the operator in interrupting the dressing process at the optimal moment, thereby optimizing grinding quality and reducing operational costs.

This study proposes a novel approach to monitoring the grinding wheel during the dressing operation by using acoustic emission (AE) signals and the statistical Counts method. AE signals were acquired during the dressing passes and processed in MATLAB^®. The Counts matrices were segmented according to the grinding wheel rotation, and the metric termed z-ratio, which combines mean and standard deviation statistics, was calculated for each subwindow. The vectors were then filtered, normalized, and represented in polar coordinates. The results demonstrate the method’s ability to track the evolution of dressing and detect grinding wheel eccentricity, offering a promising tool for the indirect monitoring of the surface conditions of the grinding tool during the dressing operation of conventional grinding wheels.

The real-time monitoring of manufacturing processes is essential to achieve high-quality standards and uniformity at reduced costs. The monitoring of machining processes is usually performed by acoustic emission sensors, which measure the dynamic waves of mechanical stress that propagate through the material during the grinding process. This work studies the application of knock sensors, commonly used in the monitoring of combustion engines, as a low-cost and high-robustness alternative to acoustic emission sensors in the monitoring of the grinding process. Built to operate mechanically attached to the engine block, the knock sensor is resistant to high temperatures, liquids, and particles. In order to demonstrate the feasibility of the sensor, different tests were performed. By determining its resonant frequency, it was possible to identify the response curve of the sensor and its optimal operating range. The acoustic impulse test, generated by the pencil lead break technique, allowed the comparative analysis of the spectral performance between the acoustic emission sensor and the knock sensor. The performance of the knock sensor in an industrial environment was also verified in a case study. The results showed a similar spectral behavior between both sensors when subjected to the same stimulus, demonstrating the feasibility of using the knock sensor to monitor the grinding process.

The quality control of the workpieces through reliable tests is of utmost importance in the manufacturing of a precision component. In this context, the monitoring of damages towards automation of the grinding process is essential for the manufacturing industry and of great interest for researchers of the area. This work proposes a technique for monitoring damages in steel parts in the grinding process, using low-cost piezoelectric diaphragms in the emitter and receiver configuration. The tests were performed in a surface grinding machine with aluminum oxide grinding wheel and SAE 4340 steel workpiece. The hardness measurements of the workpieces were carried out to identify the changes occurred from the grinding process. The signals from the transducers were sampled at a rate of 2 MHz. A spectrum analysis of the obtained signals was performed with the aim at characterizing the frequency ranges that were most related to the workpiece surface condition. The results demonstrated that the proposed method is effective to detect surface damage in steel parts in the grinding process.